RESEARCH ARTICLE Cervical Cancer Mortality Trends in China

1 downloads 0 Views 584KB Size Report
Cervical Cancer Mortality Trends and Future Predictions in China. Asian Pac J .... account changing trends, periodic changes, and random ..... 3.8c (1.4,6.3).

DOI: Cervical Cancer Mortality Trends and Future Predictions in China

RESEARCH ARTICLE Cervical Cancer Mortality Trends in China, 1991-2013, and Predictions for the Future Pei-Ling Du1, Ku-Sheng Wu1, Jia-Ying Fang1, Yang Zeng2, Zhen-Xi Xu2, WenRui Tang1, Xiao-Ling Xu2, Kun Lin1* Abstract Background: To analyze cervical cancer mortality trends in China from 1991-2013 and forecast the mortality distribution in future five years (2014-2018), and provide clues for prevention and treatment. Materials and Methods: Mortality data for cervical cancer in China from 1991 to 2013 were used to describe the epidemiological characteristics and distribution, including the trend of the standardized mortality rate, urban-rural differences, and age variation. Trend-surface analysis was used to analyze the geographical distribution of mortality. Curve estimation, time series, gray modeling, and joinpoint regression were performed to predict and forecast mortality trends. Results: In recent years, the mortality rate of cervical cancer has increased, and there is also a steady increase in the incidence from 2003 to 2013 in China. Mortality rates in rural areas are higher than in urban areas. The mortality dramatically increases in the 40+ yr age group, reaching a peak in the >85 yr age group. In addition, geographical analysis showed that the cervical cancer mortality increased from the southwest to west-central and from the southeast to northeast of the country. Conclusions: The incidence rate and the mortality rate are increasing from 1991 to 2013, and the predictions show this will continue in the future. Thus, implementation of prevention and management programs for cervical cancer are necessary in China, especially for rural areas, young women in urban areas, and high risk regions (the west-central). Keywords: Cervical cancer - epidemiological characteristics - geographic distribution - standardized mortality Asian Pac J Cancer Prev, 16 (15), 6391-6396

Introduction Worldwide, cervical cancer is the second most frequently diagnosed cancer in female, and it is the third leading cause of death for women in less developed country (Torre et al., 2015). Among the estimated numbers of almost 500,000 incident cases and 270,000 deaths due to cervical cancer annually, more than 90% of cases occur in developing countries (Ferlay et al., 2010; Torre et al., 2015). According to the China’s health statistic database, the mortality of cervical cancer ranks the eighth among malignant tumors in China. In last decade, the mortality of cervical cancer had significantly decreased compared with the 1970s, however, the incidence of cervical cancer among younger woman is increasing, and the mortality rates of rural China still keep high, such as Shanxi (Yang, 2003; Shi et al., 2012). It is still a serious public health issue in China. Many epidemiological studies had revealed the tendency of incidence and mortality for cervical cancer by describing population, age, gender, areas, etc. The epidemiological characteristics of cervical cancer are continually changing. According to these investigations, cervical cancer was associated with relevant risk factors include human papillomavirus virus (HPV), socio-

economic context, health behavior, and smoking (Gonzaga et al., 2013); these risk factors also have an impact on the prevalence of cervical cancer in China. Focused on this major public health problem in China, the present study aimed to explore the incidence and mortality trends of cervical cancer for woman in China, and to provide useful information for mapping our strategies for cervical cancer prevention, epidemiological research of cervical cancer, and cervical cancer control implementation and evaluation. Trend surface analysis as a prospective method developed in recent years has been used in epidemiological studies. Trends composed of trend values represent the systematic variation of the surface area, generally considered to be due to systematic changes in the environment caused by changes in the distribution or population (Luo et al., 2008). Trend surface reflect the area variation of disease, which is the geographic distribution characteristics of the disease and causes and influencing factors, will provide important clues for epidemiologic study of cervical cancer (Zheng and Chen, 2001). We obtained available data from the National Central Cancer Registry (NCCR) of China, and used the trend surface analysis to describe the geographical distribution and epidemiological characteristics of cervical cancer.

Department of Preventive Medicine, Shantou University Medical College, 2Shantou University Medical College, Shantou, China *For correspondence: [email protected] 1

Asian Pacific Journal of Cancer Prevention, Vol 16, 2015


Pei-Ling Du et al

Materials and Methods Data source Data was obtained from the nation-wide cancer mortality survey during the period 1991-2013 by the National Cancer Control Office, Ministry of Public Health. The data were checked and evaluated by NCCR based on “Guideline for Chinese Cancer Registration” and referring to relevant data quality criterion of “Cancer Incidence in Five Continents Volume IX” by IARC/IACR. Combined with the actual situation of cancer registration work in China, evaluation of the quality of cancer registry data was based on its integrity, reliability and validity. Data were collected by doctors with specialist knowledge. Through a goodness of fit test, it shows that there is not statistically significant difference between the regional samples and the overall. Population composition and age, sex ratios are reasonable enough to represent the national situation. To ensure reliability, estimates of the resident population on the basis of official censuses, were also derived from the same database as available to the electronic support, and available to the general public as part of registration system. All cancer cases were coded according to the International Classification of Diseases, 10th Revision (ICD-10) (code C53, for deaths because of cervical cancer) (Chen et al., 2015). The quality of these national sample surveys is credible. (China Health Statistics Yearbook, 2006; 2007; 2008; Chen, 2008; Zhao and Chen, 2008; China Health Statistics Yearbook, 2009; Zhao and Chen, 2009; 2010; 2010; China Health Statistics Yearbook, 2011; Han et al., 2011; Hao and Chen, 2012; China Health Statistics Yearbook, 2012; 2013; 2014) Statistical models Trend surface analysis. It was used to describe the geographic distribution of cervical cancer in China. On the basic of 40 registered cities/towns from 20072009 in China, we established a two-dimensional space coordinates. The space coordinates were longitude (x) and latitude (y), and the variable (z) was the mean of the cervical cancer mortality of the monitoring point, so that the mortality rates of these monitoring points have only a matching point coordinates. The method of polynomial regression was used to fit a trend surface, and the regression equation was tested for significance, according to the fitting function, and drawing contour map. Trend surface analysis and contour map drawing were performed by SAS 9.0 (SAS Institute Inc., Cary, USA). Four models (curve estimation, time series, gray modeling (GM) and joinpoint regression) were used to estimate the trend of the cervical cancer mortality from 1991 to 2013 and to predict the following 5-year trends. These four models can predict information to establish an early warning system for the prevention of cervical cancer and provide effective scientific basis for prevention and control of cervical cancer. Statistical analysis was conducted using SPSS 19.0, DPS 7.05 and Joinpoint 4.01. Curve estimation. There are 11 different models of curve estimation in SPSS, including linear, logarithmic, inverse, quadratic, cubic, power, compound, S-curve, logistic, growth and exponential models. The curve


Asian Pacific Journal of Cancer Prevention, Vol 16, 2015

estimation was used to quickly complete the 11 models parameter estimation by SPSS and display the corresponding statistic for choosing the best regression model. In this regression analysis, x stands for the time (year) and y stands for standardized mortality rates (SMR). Time-series analysis. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the mortality of diseases; it can take into account changing trends, periodic changes, and random disturbances in time series (Zheng et al., 2015). Thus in this study, ARIMA time-series analysis was used to estimate the relation between time intervals and the observed value. ARIMA includes three steps: model identification, parameter estimation and diagnostic checking. The autocorrelation function (ACF), partial autocorrelation function (PACF), mean squared error (MSE) and mean absolute deviation (MAD) were selected to be the forecasting accuracy measures. In this study, based on the data of the cervical cancer mortality from 1991 to 2013 in China, we established the ARIMA (0, 1, 0) model, which can be used to predict the cervical cancer mortality successfully. Grey model. Grey system contains not only the known information, but also information of unknown or non-known systems. Base on the gray forecast theory, GM (1, 1) model is pointed out to forecast the trend of cervical cancer in China. Even without large data, GM can effectively describe the characteristics of the few outputs using fewer (at least four number) information. The time dependent variable of the model was the number 1-23, and the independent one was the SMR, and the model was conducted by DPS software. Joinpoint regression. It also known as piecewise linear regression is to model the time series using a few continuous linear segments. These segments are joined at points called joinpoints, which represent the timing (i.e. year) for a statistically significant change in rate trend (Goovaerts, 2013). To describe linear trends by period, the estimated annual percent change (APC) and average annual percent change (AAPC) were computed for each trends of cervical cancer in China by fitting a regression line. Statistical analysis All rates of cervical cancer, expressed per 100 000 population, were directly age-adjusted to the Chinese standard population in 1982 and World Segi’s population standard.

Results Cervical cancer incidence and mortality trends from 2003 to 2013 According to the third national mortality retrospective sampling survey report, compared with the second survey results in China, cervical cancer mortality was significantly decline, the crude mortality rate was 1.89/105 in 1990s, 1.40/105 in 2000s, which is a 25.9% reduction; the SMR was 1.64/105 in 1990s, 0.94/105 in 2000s, which is a 42.7% reduction. And it ranks the sixth dropped to

the ninth in malignant tumors in China (Chen, 2008). Although the cervical cancer mortality was declined when compared to the 1990s and 1970s, but in recent years, it shows an increase from 1.22/105 in 2003 to 2.59 /105 in 2011. And there is still a steady increase in the standardized incidence of cervical cancer from 2003 to 2013, with the cervical cancer incidence being 13.4/105 in 2011, the standardized incidence rate 10.4/105, and the incidence of urban (10.6/105) was higher than rural rate (10.1/105) by 5.57%. Difference between the urban and rural cervical cancer mortality Compared to the second national mortality retrospective sampling survey report, the cervical cancer mortality of urban areas was falling by 13.5%, the SMR was falling by 31.8%, and the proportion fell to 1.40%. As well as the urban, the mortality of rural areas was falling by 29.9%, the SMR was falling by 44.2%, and the proportion fell to 2.14%. The decline degree of rural mortality is larger than those from the urban areas. But the cervical cancer mortality of rural is still higher than the urban from 1991 to 2013 (Figure 1). It shows the mortality in the rural areas keep growing from 1991 to 2013, and those in urban areas declined from 1991 to 2001, and rose again from 2002. With the joinpoint analysis, the AAPC of the SMR in rural areas was 7.5% more than that in urban areas from 2004-2013 (P

Suggest Documents